from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
print_time_report()
daal4py_KMeans_short: 0h 0m 1s
daal4py_Ridge: 0h 0m 1s
KMeans_short: 0h 0m 2s
daal4py_LogisticRegression: 0h 0m 4s
daal4py_KMeans_tall: 0h 0m 8s
Ridge: 0h 0m 10s
LogisticRegression: 0h 0m 19s
KMeans_tall: 0h 0m 22s
daal4py_KNeighborsClassifier_kd_tree: 0h 0m 27s
daal4py_KNeighborsClassifier: 0h 2m 24s
KNeighborsClassifier_kd_tree: 0h 2m 34s
lightgbm: 0h 5m 4s
HistGradientBoostingClassifier: 0h 5m 6s
catboost_lossguide: 0h 5m 9s
xgboost: 0h 5m 19s
catboost_symmetric: 0h 5m 19s
KNeighborsClassifier: 0h 32m 21s
total: 1h 4m 58s
print_env_info()
{
"system_info": {
"python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05) [GCC 9.3.0]",
"executable": "/usr/share/miniconda/envs/sklbench/bin/python",
"machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
},
"dependencies_info": {
"pip": "21.1.2",
"setuptools": "49.6.0.post20210108",
"sklearn": "1.0.dev0",
"numpy": "1.20.3",
"scipy": "1.6.3",
"Cython": null,
"pandas": "1.2.4",
"matplotlib": "3.4.2",
"joblib": "1.0.1",
"threadpoolctl": "2.1.0"
},
"threadpool_info": [
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
"prefix": "libopenblas",
"user_api": "blas",
"internal_api": "openblas",
"version": "0.3.15",
"num_threads": 2,
"threading_layer": "pthreads"
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
},
{
"filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
"prefix": "libgomp",
"user_api": "openmp",
"internal_api": "openmp",
"version": null,
"num_threads": 2
}
],
"cpu_count": 2
}
reporting = Reporting(config="config.yml")
reporting.run()
KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.744 | 0.000 | -1 | 100 | NaN | NaN | 0.501 | 0.000 | 0.278 | 0.000 | See | See |
| 1 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 30.387 | 0.000 | 0.000 | 0.030 | -1 | 100 | 0.941 | 0.935 | 1.755 | 0.011 | 17.313 | 0.108 | See | See |
| 2 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.179 | 0.014 | 0.000 | 0.179 | -1 | 100 | 1.000 | 1.000 | 0.090 | 0.000 | 1.997 | 0.161 | See | See |
| 3 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.130 | 0.000 | 6.149 | 0.000 | -1 | 1 | NaN | NaN | 0.485 | 0.000 | 0.268 | 0.000 | See | See |
| 4 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 24.893 | 0.244 | 0.000 | 0.025 | -1 | 1 | 0.692 | 0.713 | 1.689 | 0.007 | 14.738 | 0.156 | See | See |
| 5 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.175 | 0.012 | 0.000 | 0.175 | -1 | 1 | 0.000 | 0.000 | 0.095 | 0.011 | 1.846 | 0.242 | See | See |
| 6 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.139 | 0.000 | 5.771 | 0.000 | 1 | 100 | NaN | NaN | 0.486 | 0.000 | 0.285 | 0.000 | See | See |
| 7 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 19.666 | 0.186 | 0.000 | 0.020 | 1 | 100 | 0.941 | 0.935 | 1.759 | 0.012 | 11.181 | 0.129 | See | See |
| 8 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.202 | 0.001 | 0.000 | 0.202 | 1 | 100 | 1.000 | 1.000 | 0.090 | 0.000 | 2.247 | 0.013 | See | See |
| 9 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.131 | 0.000 | 6.094 | 0.000 | -1 | 5 | NaN | NaN | 0.485 | 0.000 | 0.271 | 0.000 | See | See |
| 10 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 30.062 | 0.167 | 0.000 | 0.030 | -1 | 5 | 0.822 | 0.806 | 1.693 | 0.008 | 17.760 | 0.132 | See | See |
| 11 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.183 | 0.018 | 0.000 | 0.183 | -1 | 5 | 1.000 | 1.000 | 0.091 | 0.000 | 2.021 | 0.201 | See | See |
| 12 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.136 | 0.000 | 5.862 | 0.000 | 1 | 5 | NaN | NaN | 0.485 | 0.000 | 0.281 | 0.000 | See | See |
| 13 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 19.749 | 0.352 | 0.000 | 0.020 | 1 | 5 | 0.822 | 0.713 | 1.692 | 0.010 | 11.670 | 0.220 | See | See |
| 14 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.208 | 0.002 | 0.000 | 0.208 | 1 | 5 | 1.000 | 0.000 | 0.091 | 0.001 | 2.300 | 0.024 | See | See |
| 15 | KNeighborsClassifier | fit | 1000000 | 1000000 | 100 | NaN | 0.135 | 0.000 | 5.917 | 0.000 | 1 | 1 | NaN | NaN | 0.485 | 0.000 | 0.278 | 0.000 | See | See |
| 16 | KNeighborsClassifier | predict | 1000000 | 1000 | 100 | NaN | 13.464 | 0.195 | 0.000 | 0.013 | 1 | 1 | 0.692 | 0.806 | 1.690 | 0.007 | 7.965 | 0.120 | See | See |
| 17 | KNeighborsClassifier | predict | 1000000 | 1 | 100 | NaN | 0.203 | 0.001 | 0.000 | 0.203 | 1 | 1 | 0.000 | 1.000 | 0.095 | 0.010 | 2.146 | 0.233 | See | See |
| 18 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.286 | 0.000 | -1 | 100 | NaN | NaN | 0.098 | 0.000 | 0.568 | 0.000 | See | See |
| 19 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.772 | 0.000 | 0.000 | 0.031 | -1 | 100 | 0.986 | 0.985 | 0.302 | 0.002 | 101.743 | 0.759 | See | See |
| 20 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.029 | 0.002 | 0.000 | 0.029 | -1 | 100 | 1.000 | 1.000 | 0.005 | 0.001 | 5.483 | 0.616 | See | See |
| 21 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.056 | 0.000 | 0.286 | 0.000 | -1 | 1 | NaN | NaN | 0.099 | 0.000 | 0.562 | 0.000 | See | See |
| 22 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 22.260 | 0.117 | 0.000 | 0.022 | -1 | 1 | 0.975 | 0.980 | 0.254 | 0.001 | 87.491 | 0.555 | See | See |
| 23 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.019 | 0.003 | 0.000 | 0.019 | -1 | 1 | 1.000 | 1.000 | 0.005 | 0.000 | 3.769 | 0.636 | See | See |
| 24 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.294 | 0.000 | 1 | 100 | NaN | NaN | 0.098 | 0.000 | 0.553 | 0.000 | See | See |
| 25 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 18.201 | 0.528 | 0.000 | 0.018 | 1 | 100 | 0.986 | 0.985 | 0.303 | 0.002 | 60.081 | 1.804 | See | See |
| 26 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.020 | 0.001 | 0.000 | 0.020 | 1 | 100 | 1.000 | 1.000 | 0.005 | 0.000 | 4.000 | 0.393 | See | See |
| 27 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.051 | 0.000 | 0.314 | 0.000 | -1 | 5 | NaN | NaN | 0.099 | 0.000 | 0.515 | 0.000 | See | See |
| 28 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 30.500 | 0.000 | 0.000 | 0.030 | -1 | 5 | 0.981 | 0.984 | 0.258 | 0.002 | 118.374 | 0.715 | See | See |
| 29 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.029 | 0.002 | 0.000 | 0.029 | -1 | 5 | 1.000 | 1.000 | 0.005 | 0.001 | 5.876 | 0.773 | See | See |
| 30 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.054 | 0.000 | 0.296 | 0.000 | 1 | 5 | NaN | NaN | 0.099 | 0.000 | 0.545 | 0.000 | See | See |
| 31 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 19.224 | 0.044 | 0.000 | 0.019 | 1 | 5 | 0.981 | 0.980 | 0.255 | 0.001 | 75.514 | 0.319 | See | See |
| 32 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.022 | 0.001 | 0.000 | 0.022 | 1 | 5 | 1.000 | 1.000 | 0.005 | 0.000 | 4.477 | 0.465 | See | See |
| 33 | KNeighborsClassifier | fit | 1000000 | 1000000 | 2 | NaN | 0.055 | 0.000 | 0.293 | 0.000 | 1 | 1 | NaN | NaN | 0.099 | 0.000 | 0.553 | 0.000 | See | See |
| 34 | KNeighborsClassifier | predict | 1000000 | 1000 | 2 | NaN | 10.780 | 0.005 | 0.000 | 0.011 | 1 | 1 | 0.975 | 0.984 | 0.256 | 0.001 | 42.110 | 0.112 | See | See |
| 35 | KNeighborsClassifier | predict | 1000000 | 1 | 2 | NaN | 0.015 | 0.001 | 0.000 | 0.015 | 1 | 1 | 1.000 | 1.000 | 0.005 | 0.001 | 2.853 | 0.315 | See | See |
KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | n_jobs | n_neighbors | accuracy_score_sklearn | accuracy_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.799 | 0.000 | 0.029 | 0.000 | 1 | 100 | NaN | NaN | 0.709 | 0.000 | 3.949 | 0.000 | See | See |
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 4.667 | 0.008 | 0.000 | 0.005 | 1 | 100 | 0.978 | 0.974 | 0.184 | 0.003 | 25.354 | 0.395 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.004 | 0.001 | 0.000 | 0.004 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 9.943 | 5.496 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.831 | 0.000 | 0.028 | 0.000 | -1 | 5 | NaN | NaN | 0.671 | 0.000 | 4.218 | 0.000 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.808 | 0.006 | 0.000 | 0.001 | -1 | 5 | 0.976 | 0.963 | 0.100 | 0.001 | 8.054 | 0.096 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 10.026 | 5.172 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.804 | 0.000 | 0.029 | 0.000 | 1 | 1 | NaN | NaN | 0.672 | 0.000 | 4.172 | 0.000 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.738 | 0.005 | 0.000 | 0.001 | 1 | 1 | 0.954 | 0.974 | 0.186 | 0.002 | 3.964 | 0.054 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 2.828 | 1.485 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.848 | 0.000 | 0.028 | 0.000 | 1 | 5 | NaN | NaN | 0.682 | 0.000 | 4.176 | 0.000 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 1.409 | 0.009 | 0.000 | 0.001 | 1 | 5 | 0.976 | 0.969 | 0.539 | 0.012 | 2.616 | 0.060 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 5 | 1.000 | 1.000 | 0.001 | 0.000 | 1.494 | 0.723 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.876 | 0.000 | 0.028 | 0.000 | -1 | 1 | NaN | NaN | 0.693 | 0.000 | 4.150 | 0.000 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 0.432 | 0.005 | 0.000 | 0.000 | -1 | 1 | 0.954 | 0.969 | 0.533 | 0.004 | 0.811 | 0.011 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 1.000 | 1.000 | 0.001 | 0.000 | 2.272 | 0.951 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | NaN | 2.957 | 0.000 | 0.027 | 0.000 | -1 | 100 | NaN | NaN | 0.671 | 0.000 | 4.407 | 0.000 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | NaN | 2.722 | 0.032 | 0.000 | 0.003 | -1 | 100 | 0.978 | 0.963 | 0.100 | 0.001 | 27.158 | 0.385 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | NaN | 0.006 | 0.001 | 0.000 | 0.006 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 18.861 | 9.501 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.715 | 0.000 | 0.022 | 0.000 | 1 | 100 | NaN | NaN | 0.427 | 0.000 | 1.675 | 0.000 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.045 | 0.003 | 0.000 | 0.000 | 1 | 100 | 0.984 | 0.983 | 0.001 | 0.000 | 42.852 | 12.674 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 5.642 | 4.458 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.714 | 0.000 | 0.022 | 0.000 | -1 | 5 | NaN | NaN | 0.427 | 0.000 | 1.674 | 0.000 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.026 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.982 | 0.976 | 0.001 | 0.000 | 35.545 | 10.221 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 19.443 | 14.759 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.721 | 0.000 | 0.022 | 0.000 | 1 | 1 | NaN | NaN | 0.464 | 0.000 | 1.553 | 0.000 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.023 | 0.001 | 0.001 | 0.000 | 1 | 1 | 0.977 | 0.983 | 0.001 | 0.000 | 21.664 | 6.308 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 5.629 | 4.534 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.721 | 0.000 | 0.022 | 0.000 | 1 | 5 | NaN | NaN | 0.436 | 0.000 | 1.653 | 0.000 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.001 | 0.001 | 0.000 | 1 | 5 | 0.982 | 0.985 | 0.006 | 0.001 | 3.925 | 0.439 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 1.000 | 1.000 | 0.000 | 0.000 | 5.136 | 4.126 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.730 | 0.000 | 0.022 | 0.000 | -1 | 1 | NaN | NaN | 0.428 | 0.000 | 1.705 | 0.000 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.024 | 0.001 | 0.001 | 0.000 | -1 | 1 | 0.977 | 0.985 | 0.006 | 0.001 | 3.920 | 0.524 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 1.000 | 1.000 | 0.000 | 0.000 | 18.744 | 14.277 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 2 | NaN | 0.734 | 0.000 | 0.022 | 0.000 | -1 | 100 | NaN | NaN | 0.433 | 0.000 | 1.694 | 0.000 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 2 | NaN | 0.042 | 0.003 | 0.000 | 0.000 | -1 | 100 | 0.984 | 0.976 | 0.001 | 0.000 | 56.556 | 19.792 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 2 | NaN | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 1.000 | 1.000 | 0.000 | 0.000 | 20.708 | 17.103 | See | See |
KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.623 | 0.0 | 0.771 | 0.000 | k-means++ | NaN | 30 | NaN | 0.409 | 0.0 | 1.523 | 0.000 | See | See |
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.377 | 0.000 | k-means++ | 0.000 | 30 | 0.001 | 0.000 | 0.0 | 8.521 | 5.436 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.311 | 8.181 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 30 | 0.496 | 0.0 | 0.967 | 0.000 | random | NaN | 30 | NaN | 0.353 | 0.0 | 1.405 | 0.000 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 30 | 0.001 | 0.0 | 0.381 | 0.000 | random | 0.001 | 30 | 0.001 | 0.000 | 0.0 | 9.235 | 6.658 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 30 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.825 | 8.362 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 6.550 | 0.0 | 3.664 | 0.000 | k-means++ | NaN | 30 | NaN | 3.096 | 0.0 | 2.116 | 0.000 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.617 | 0.000 | k-means++ | 0.002 | 30 | 0.003 | 0.000 | 0.0 | 5.880 | 2.944 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | k-means++ | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.353 | 7.734 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 30 | 5.869 | 0.0 | 4.089 | 0.000 | random | NaN | 30 | NaN | 2.940 | 0.0 | 1.996 | 0.000 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 30 | 0.002 | 0.0 | 15.800 | 0.000 | random | 0.003 | 30 | 0.002 | 0.000 | 0.0 | 5.719 | 3.094 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 30 | 0.001 | 0.0 | 0.020 | 0.001 | random | 1.000 | 30 | 1.000 | 0.000 | 0.0 | 10.287 | 7.725 | See | See |
KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | n_iter_sklearn | mean_sklearn | stdev_sklearn | throughput | latency | init | adjusted_rand_score_sklearn | n_iter_daal4py | adjusted_rand_score_daal4py | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.073 | 0.0 | 0.044 | 0.000 | random | NaN | 20 | NaN | 0.031 | 0.0 | 2.355 | 0.000 | See | See |
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.190 | 0.000 | random | 0.002 | 20 | 0.004 | 0.001 | 0.0 | 2.689 | 0.484 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.180 | 7.546 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 20 | 0.219 | 0.0 | 0.015 | 0.000 | k-means++ | NaN | 20 | NaN | 0.083 | 0.0 | 2.624 | 0.000 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 20 | 0.002 | 0.0 | 0.192 | 0.000 | k-means++ | -0.000 | 20 | 0.002 | 0.001 | 0.0 | 2.613 | 0.610 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 20 | 0.001 | 0.0 | 0.000 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 10.159 | 7.500 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.190 | 0.0 | 0.844 | 0.000 | random | NaN | 20 | NaN | 0.127 | 0.0 | 1.498 | 0.000 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.663 | 0.000 | random | 0.344 | 20 | 0.324 | 0.001 | 0.0 | 2.182 | 0.329 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | random | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 7.989 | 4.859 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 20 | 0.562 | 0.0 | 0.285 | 0.000 | k-means++ | NaN | 20 | NaN | 0.314 | 0.0 | 1.788 | 0.000 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 20 | 0.002 | 0.0 | 6.631 | 0.000 | k-means++ | 0.338 | 20 | 0.316 | 0.001 | 0.0 | 2.204 | 0.357 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 20 | 0.001 | 0.0 | 0.012 | 0.001 | k-means++ | 1.000 | 20 | 1.000 | 0.000 | 0.0 | 8.218 | 5.050 | See | See |
LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | class_weight | l1_ratio | n_jobs | random_state | accuracy_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | [20] | 11.018 | 0.0 | [-0.10708741] | 0.000 | NaN | NaN | NaN | NaN | NaN | 2.023 | 0.0 | 5.446 | 0.000 | See | See |
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | [20] | 0.000 | 0.0 | [52.5941921] | 0.000 | NaN | NaN | NaN | NaN | 0.546 | 0.000 | 0.0 | 0.888 | 0.510 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | [20] | 0.000 | 0.0 | [0.21355686] | 0.000 | NaN | NaN | NaN | NaN | 1.000 | 0.000 | 0.0 | 0.422 | 0.377 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | [34] | 0.989 | 0.0 | [-1.59278656] | 0.001 | NaN | NaN | NaN | NaN | NaN | 0.727 | 0.0 | 1.360 | 0.000 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | [34] | 0.002 | 0.0 | [172.06900068] | 0.000 | NaN | NaN | NaN | NaN | 0.240 | 0.003 | 0.0 | 0.556 | 0.137 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | [34] | 0.000 | 0.0 | [29.61295219] | 0.000 | NaN | NaN | NaN | NaN | 0.000 | 0.001 | 0.0 | 0.127 | 0.101 | See | See |
Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)¶All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | n_iter | mean_sklearn | stdev_sklearn | throughput | latency | max_iter | random_state | r2_score | mean_daal4py | stdev_daal4py | speedup | stdev_speedup | sklearn_profiling | daal4py_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | NaN | 0.178 | 0.000 | 0.449 | 0.0 | NaN | NaN | NaN | 0.178 | 0.000 | 1.002 | 0.000 | See | See |
| 1 | Ridge | predict | 1000 | 1000 | 10000 | NaN | 0.011 | 0.001 | 6.961 | 0.0 | NaN | NaN | 0.095 | 0.019 | 0.001 | 0.616 | 0.037 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | NaN | 0.000 | 0.000 | 1.206 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.715 | 0.749 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | NaN | 1.444 | 0.000 | 0.554 | 0.0 | NaN | NaN | NaN | 0.238 | 0.000 | 6.061 | 0.000 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | NaN | 0.000 | 0.000 | 5.818 | 0.0 | NaN | NaN | 1.000 | 0.000 | 0.000 | 0.633 | 0.485 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | NaN | 0.000 | 0.000 | 0.015 | 0.0 | NaN | NaN | NaN | 0.000 | 0.000 | 0.665 | 0.743 | See | See |